Access to energy services is essential for preserving health and well-being. However, energy poverty is a challenge affecting millions of citizens worldwide, which could even worsen due to the predicted severity of climate change. Energy poverty vulnerability and social problems are often linked to energy-inefficient buildings. Thus, identifying energy-inefficient dwellings in energy-vulnerable urban areas is crucial for formulating and implementing effective public policies. Consequently, this study proposes a multidimensional methodological approach to determine these urban areas and support decision-making to develop public policies that can help lift dwellings out of or prevent them from falling into energy poverty. The suggested methodology utilizes public data from existing databases and applies unsupervised machine–learning classification algorithms. Applying such methodology to the case study of Seville identified different clusters of urban areas with similar characteristics, providing key information for creating specific public policies tailored to the needs of each area and community. The study’s findings support Building Renovation Wave strategies to improve energy efficiency in dwellings, define specific policies for access to financial resources for low-income families, and provide personalized support for vulnerable populations.

中文翻译：

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识别城市能源薄弱区域：机器学习方法

获得能源服务对于维护健康和福祉至关重要。然而，能源匮乏是影响全球数百万公民的挑战，由于预计气候变化的严重性，这种情况甚至可能恶化。能源贫困、脆弱性和社会问题通常与能源效率低下的建筑有关。因此，在能源脆弱的城市地区识别能源效率低下的住宅对于制定和实施有效的公共政策至关重要。因此，本研究提出了一种多维方法来确定这些城市地区并支持决策，以制定有助于使住宅摆脱能源贫困或防止其陷入能源贫困的公共政策。建议的方法利用来自现有数据库的公共数据，并应用无监督机器学习分类算法。将这种方法应用于塞维利亚的案例研究，确定了具有相似特征的不同城市地区集群，为制定针对每个地区和社区需求的具体公共政策提供了关键信息。该研究的结果支持 Building Renovation Wave 战略，以提高住宅的能源效率，确定低收入家庭获得财务资源的具体政策，并为弱势群体提供个性化支持。